Edge-enhancement densenet for X-ray fluoroscopy image denoising in cardiac electrophysiology procedures.

Journal: Medical physics
PMID: 34954836

Abstract

PURPOSE: Reducing X-ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low-dose X-ray fluoroscopy images but may compromise clinically important details required by cardiologists.

Authors

  • Yimin Luo
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Yingliang Ma
    School of Computing, Electronics and Mathematics, Coventry University, Coventry, UK.
  • Hugh O' Brien
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Kui Jiang
    School of Computer Science, Wuhan University, Wuhan, China.
  • Vikram Kohli
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Sesilia Maidelin
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Mahrukh Saeed
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Emily Deng
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Kuberan Pushparajah
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Kawal S Rhode
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Keywords

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